Data-driven Development of Calibration and High-throughput Measurement Protocols for Quantum Structures

Harold B. Meerwaldt, Daniël P. van der Velde, Pieter T. Eendebak +1 more

Quantum computers require software to control the states of the qubits. As the actions on qubits grow in complexity with circuit execution, calibration, and benchmarking, the complexity of the control software grows in tandem. Spin-qubit quantum computers are at a stage where experimentalists need low-level access for tuning and calibration, and quantum algorithm researchers want high-level circuit-level access. In this article, we present the software design of SpinControl. SpinControl integrates three architectural metrics: 1) customizability, to allow prototyping devices and measurement protocols, is achieved by separating data and behaviour; 2) modularity and extensibility are achieved by separating functionalities into different components; and 3) robustness, to reliably execute quantum circuits on many qubits, is achieved by CI pipelines and input validation. SpinControl allows different types of measurements while customizing the instruments used, the pulse or circuit program executed, and the data processing. It allows building standardized experiments from measurement and analysis building blocks and executing these for calibration in a custom and conditional order. SpinControl implements these functionalities alongside robust configuration and data management, a connection to the cloud, and calibration scheduling. Pulse and circuit compilation, virtual gate matrices, as well as spin-qubit-specific GUIs integrated by SpinControl, make heavy use of software packages developed by Delft University of Technology: pulse lib, core-tools (both open source), and QConstruct

Lassi Lehtisyrjä, R. P. Loreto, Juho Luomahaara +11 more

Coulomb blockade thermometers (CBTs) have attracted more interest in recent years, as the demand for sub-one Kelvin thermometry has increased, especially due to the prevalence of dilution refrigerators in research and applications in quantum technology. CBTs can be operated both as a primary thermometer, requiring no prior calibration, or as a simple resistance thermometer in the secondary mode after calibration. As new scalable fabrication processes for quantum devices and cryogenic electronics are being developed, cryogenic wafer characterization methods must also scale up to provide statistical data on device parameters. Currently, characterization throughput of cryogenic devices is limited by the turnover time and sample capacity of traditional cryostats, where a measurement cycle for only a few devices can take several days. In this work, we demonstrate wafer-scale cryogenic characterization of a recently developed TiW/Al-AlOx/TiW normal-metal tunnel junction technology using CBTs. Measurements performed in a 300 mm cryogenic wafer prober (CWP) show on-chip electron temperatures below 700 mK across a full 150 mm wafer, as determined by primary thermometry. These results establish wafer-level testing below 1 K as a viable approach for large-scale cryogenic characterization of electrical devices, opening a pathway toward high-throughput screening of quantum devices and direct wafer-scale characterization of aluminum-based superconducting circuits.

Alicia B Magann, Nathan Eli Miller, Robin Blume-Kohout +2 more

Ewout van den Berg, Brad Mitchell, Ken Xuan Wei +1 more

Learned open-quantum-system models have broad applications in device characterization, device calibration, realistic noise model construction, and bounding observable estimates in quantum simulation. To this end, we develop a highly scalable Lindblad learning protocol assuming an effective time-independent Lindblad generator with local interactions. For a given operation that can be applied repeatedly on a quantum computer, the protocol learns the Lindbladian by forming a linear system of equations for the model parameters in terms of a set of observable values and their gradients. The required gradient information is obtained by fitting time-series data with sums of exponentially damped sinusoids and differentiating those curves. We develop a robust curve-fitting procedure that finds a parsimonious representation of the data up to shot noise. We demonstrate the approach by learning the Lindbladian for a full layer of gates on a 156-qubit superconducting quantum processor, providing the first learning experiment of this kind. We study the effects of state-preparation and measurement errors and limitations on the operations that can be learned. For improved performance under readout errors, we propose an optional fine-tuning strategy that improves the fit between the time-evolved model and the measured data.

Jacob M. Taylor